Device and method for training a model

ABSTRACT

A device and a method for training a model are disclosed, wherein the method of training the model includes: first classifying a plurality of data packets using the model, wherein a first class is assigned to each data packet of a plurality of data packets, wherein the first class is associated with a receiver of a plurality of receivers; second classifying the plurality of data packets, wherein a second class is assigned to each data packet of the plurality of data packets, wherein the second class is associated with a receiver of the plurality of receivers; and training the model using the plurality of first classes and the plurality of second classes assigned to the plurality of data packets.

Various embodiments generally relate to a device and a method fortraining a model.

By way of example, in wireless communication, data packets or datablocks are generated by a transmitter and transmitted wirelessly througha communication channel to a receiving device including a plurality ofreceivers, wherein the complexity of the architecture of each receivermay vary and wherein each data packet may require a minimum complexityof a receiver architecture depending on the features of the respectivedata packet. Thus, it may be necessary to provide a system capable ofselecting a receiver (i.e. a receiver architecture) for a respectivedata packet depending on the features of the data packet.

A method of computer-implemented training a model may include: firstclassifying a plurality of data packets using the model, wherein a firstclass is assigned to each data packet of a plurality of data packets,wherein the first class is associated with a receiver of a plurality ofreceivers; second classifying the plurality of data packets, wherein asecond class is assigned to each data packet of the plurality of datapackets, wherein the second class is associated with a receiver of theplurality of receivers; and training the model using the plurality offirst classes and the plurality of second classes assigned to theplurality of data packets. The method mentioned in this paragraphprovides a first example.

The method enables a model to be trained to select a receiver for a datapacket depending on the features of the data packet.

A model may be any kind of algorithm, which provides output data forinput data. A model may be a differential model. For example, a modelmay be a support vector machine or a neural network, such as forwardthinking neural network or a sum-product neural network.

The plurality of data packets may include communication data. Thefeature mentioned in this paragraph in combination with the firstexample provides a second example.

Each data packet of the plurality of data packets may include IQ data.The feature mentioned in this paragraph in combination with the firstexample or the second example provides a third example.

Each receiver of the plurality of receivers may include a complexitylevel of a plurality of complexity levels. The feature mentioned in thisparagraph in combination with any one of the first example to the thirdexample provides a fourth example.

Each data packet of the plurality of data packets may require a receiverof the plurality of receivers with a minimum complexity level to beprocessed without an error. The feature mentioned in this paragraph incombination with the fourth example provides a fifth example.

The second classifying may include processing each data packet of theplurality of data packets using the plurality of receivers. The secondclassifying may further include assigning to each data packet of theplurality of data packets the second class associated to a receiver ofthe plurality of receivers including the minimum complexity level. Thefeatures mentioned in this paragraph in combination with the fifthexample provide a sixth example.

Each data packet of the plurality of data packets may be processed usingthe plurality of receivers successively starting with the receiverincluding the lowest complexity level and continuing with the receiverincluding the next lowest complexity level until a receiver of thecomplexity of receivers processes the respective data packet without anerror. The complexity of the receiver, which processes the respectivedata packet without an error, may be the minimum complexity level. Thefeatures mentioned in this paragraph in combination with the sixthexample provide a seventh example.

Training the model may include adapting the model by comparing the firstclass with the second class assigned to each data packet of theplurality of data packets. The feature mentioned in this paragraph incombination with any one of the first example to the seventh exampleprovides an eighth example.

Training the model may further include adapting the model such that thefirst class assigned to each data packet of the plurality of datapackets corresponds to the second class. The feature mentioned in thisparagraph in combination with the eighth example provides a ninthexample.

The model may be a neural network. The feature mentioned in thisparagraph in combination with any one of the first example to the ninthexample provides a tenth example.

A receiving device may be configured to perform the method of any one ofthe first example to the tenth example. The receiving device mentionedin this paragraph provides an eleventh example.

A data reception method may include classifying a plurality of datapackets using a model, wherein the model may be trained by the method ofany one of the first example to the tenth example, wherein a class maybe assigned to each data packet of the plurality of data packets andwherein the class may be associated with a receiver of a plurality ofreceivers. The assigned class may be associated to the receiverincluding a minimum complexity level required to process the respectivedata packet. The data reception method may further include processingeach data packet of the plurality of data packets using the receiver ofthe plurality of receivers associated to the respective class of theplurality of classes. This has the advantage that a data packet isprocessed by a most appropriate receiver in terms of capability andleast complexity. In other words, a data packet is neither processedstage-wise starting with the receiver having the lowest complexity untila receiver is capable of processing the data packet nor are the datapackets processed by a high complexity receiver if the data packet doesnot require the high complexity receiver. Thus, this has the advantageof reducing the processing cost and the power consumption. Hence, it isan aspect of various embodiments to provide a receiving device capableof reducing the power consumption necessary for processing (for exampledecoding) data packets. Another advantage of the data reception methodis that a combination of low complexity receivers and high complexityreceivers can be used while the performance is the same as for highcomplexity receivers. The data reception method mentioned in thisparagraph provides a twelfth example.

The data reception method may further include estimating a processingtime and/or a processing cost based on the classification of theplurality of data packets before processing the plurality of datapackets. The feature mentioned in this paragraph in combination with thetwelfth example provides a thirteenth example.

A system may include a receiving device including a model trained by themethod of any one of the first example to the tenth example. Thereceiving device may be configured to receive and to process datapackets. The system may further include at least one transmitter deviceconfigured to transmit data packets to the receiving device. The systemmentioned in this paragraph provides a fourteenth example.

The system may be a multiple input multiple output (MIMO) system. Thefeature mentioned in this paragraph in combination with the fourteenthexample provides a fifteenth example.

Various embodiments of the invention are described with reference to thefollowing drawings, in which:

FIG. 1A shows a system according to various embodiments;

FIG. 1B shows a processing system for generating data packets accordingto various embodiments;

FIG. 2 shows a processing system for training a model according tovarious embodiments;

FIG. 3 shows a processing system for generating training data accordingto various embodiments;

FIG. 4 shows a processing system for generating training data accordingto various embodiments;

FIG. 5 shows a method for training a model according to variousembodiments;

FIG. 6 shows a system including a trained model according to variousembodiments;

FIG. 7 shows a receiving device including a trained model according tovarious embodiments.

In an embodiment, a “circuit” may be understood as any kind of a logicimplementing entity, which may be hardware, software, firmware, or anycombination thereof. Thus, in an embodiment, a “circuit” may be ahard-wired logic circuit or a programmable logic circuit such as aprogrammable processor, e.g. a microprocessor (e.g. a ComplexInstruction Set Computer (CISC) processor or a Reduced Instruction SetComputer (RISC) processor). A “circuit” may also be software beingimplemented or executed by a processor, e.g. any kind of computerprogram, e.g. a computer program using a virtual machine code such ase.g. Java. Any other kind of implementation of the respective functionswhich will be described in more detail below may also be understood as a“circuit” in accordance with an alternative embodiment.

In wireless communication a receiving device may include a plurality ofreceivers, wherein each receiver may have a different complexity levelof the architecture. The receiving device may receive data packetstransmitted by a transmitting device, wherein each data packet mayrequire a minimum complexity level of a receiver architecture.Illustratively, a model is trained to select a receiver for a respectivedata packet depending on the features of the data packet.

FIG. 1A shows a system 100A according to various embodiments. The system100A may include a transmitting device 102. The transmitting device 102may be configured to provide a plurality of data packets 104 (i.e. datablocks). The plurality of data packets 104 may include communicationdata. The plurality of data packets 104 may include IQ-data (or. I/Qdata). The transmitting device 102 may be configured to generate theplurality of data packets 104. The system 100A may further include areceiving device 106. The receiving device 106 may be included in a basestation. The transmitting device 102 may be configured to provide theplurality of data packets 104 to the receiving device 106. Thetransmitting device may be configured to transmit the plurality of datapackets wirelessly (for example over the air) to the receiving device106 using a wireless communication channel (for example a radiofrequency (RF) communication channel) and the receiving device 106 maybe configured to receive the plurality of data packets 106 wirelesslyfrom the transmitting device 102.

The receiving device 106 may include a memory device 108. The memorydevice 108 may include a memory which is for example used in theprocessing carried out by a processor. A memory used in the embodimentsmay be a volatile memory, for example a DRAM (Dynamic Random AccessMemory) or a non-volatile memory, for example a PROM (Programmable ReadOnly Memory), an EPROM (Erasable PROM), EEPROM (Electrically ErasablePROM), or a flash memory, e.g., a floating gate memory, a chargetrapping memory, an MRAM (Magnetoresistive Random Access Memory) or aPCRAM (Phase Change Random Access Memory). The memory device 108 may beconfigured to store the plurality of data packets 104 provided by thetransmitting device 102. The system 100A may further include at leastone processor 110. The at least one processor 110 may be any kind ofcircuit, i.e. any kind of logic implementing entity, as described above.In various embodiments, the processor 110 may be configured to processthe plurality of data packets 104.

FIG. 1B shows a processing system 100B for generating data packetsaccording to various embodiments. The processing system 100B may includethe memory device 108. The memory device 108 may store transmissionparameters 112. The transmission parameters may include specifictransmission features, for example different waveforms, such asorthogonal frequency division multiplex (OFDM) or generalized frequencydivision multiplex (GFDM) but not limited to them, different modulationtypes, such as quadrature phase-shift keying (QPSK), binary phase-shiftkeying (BPSK), quadrature amplitude modulation (QAM), for example 64QAM,etc., different bandwidth, different transmission power levels,different multiple input multiple output (MIMO) scenarios (1×1), (1×2),(2×1), (2×2), (4×4) etc. The transmission parameters may includetransmission channel conditions, which may be affected by inter-symbolinterference (ISI), inter carrier interference (ICI) and/or interantenna interference (IAI). The processing system 100B may furtherinclude the at least one processor 110. The processor 110 may beconfigured to process the transmission parameters 112. The processor 110may be configured to implement at least a part of a simulation model114. the simulation model 114 may be configured to simulate a pluralityof data packets 104 using the transmission parameters 112. Thesimulation model 114 may be any kind of simulation model (for example aphysical model). The simulation model may be any kind of code, which, ifimplemented by a processor, is capable of simulating data packets. Thesimulation performed by the simulation model may be a static or adynamic simulation, a stochastic or deterministic simulation. The memorydevice 108 may be further configured to store the plurality of datapackets 104 generated by the simulation model 114.

FIG. 2 shows a processing system 200 for training a model according tovarious embodiments. The processing system 200 may include the memorydevice 108. The memory device 108 may store the plurality of datapackets 104, wherein the plurality of data packets may be provided tothe memory device 108 using the transmitting device 102 according to thesystem 100A or using the simulation model 114 according to theprocessing system 100B. The processing system 200 may further includethe at least one processor 110. The at least one processor 110 may beconfigured to process the plurality of data packets 104. The processor110 may be configured to implement at least a part of a model 202. Themodel 202 may be a neural network, wherein the neural network may be anykind of neural network, such as a convolutional neural network, aforward thinking neural network or a sum-product neural network. Theneural network may include any number of layers and the training of theneural network, i.e. adapting the layers of the neural network, may bebased on any kind of training principle, such as backpropagation, i.e.the backpropagation algorithm. The model 202 may be configured toprocess the plurality of data packets 104 and may be further configuredto provide a plurality of first classified data packets 204. Theplurality of first classified data packets 204 may include a first classof a plurality of first classes assigned to each data packet of theplurality of data packets 104. In other words, the plurality of firstclassified data packets 204 may include each data packet of theplurality of data packets 104 labeled with a first class. Hence,according to various embodiments, the model 202 is configured toclassify data packets, such as raw IQ data used in communication. Eachfirst class of the plurality of first classes may be associated with areceiver of a plurality of receivers. In other words, each receiver of aplurality of receivers may be associated to a first class of theplurality of first classes and the model 202 may be configured to assigna first class to each data packet of the plurality of data packets 104.

The memory device 108 may be further configured to store a plurality ofsecond classified data packets 206. The plurality of second classifieddata packets 206 may include a second class of a plurality of secondclasses assigned to each data packet of the plurality of data packets104. In other words, the plurality of second classified data packets 206may include each data packet of the plurality of data packets 104labeled with a second class. Each second class of the plurality ofsecond classes may be associated with a receiver of a plurality ofreceivers. In other words, each receiver of a plurality of receivers maybe associated to a second class of the plurality of second classes. Theplurality of second classified data packets 206 may be training dataused for training the model 202. Thus, the memory device 108 may beconfigured to store the training data used for training the model 202.The processor 110 may be further configured to determine aclassification loss 208. The classification loss 208 may be determinedusing the plurality of first classified data packets 204 and theplurality of second classified data packets 206. The classification loss208 may be determined by comparing the first class assigned to each datapacket of the plurality of data packets 104, i.e. the plurality of firstclassified data packets 204, with the second class assigned to each datapacket of the plurality of data packets 104, i.e. the plurality ofsecond classified data packets 206. The processor 110 may be configuredto adapt the model 202 using the classification loss 208. The processor110 may be configured to adapt the model 202 such that the plurality offirst classified data packets 204 generated by the model 202 correspondsto the plurality of second classified data packets 206. In other words,the model 202 may be adapted such that the first class assigned to eachdata packet of the plurality of data packets 104 by the model 202corresponds to the second class assigned to each data packet. In evenother words, the model 202 learns to assign a first class to a datapacket, which corresponds to the second class assigned to the respectivedata packet. Illustratively, the model 202 learns to assign a class,which is associated with a receiver, to a data packet considering thedata packet parameters, such as properties of a transmitted data patternand channel characteristics, and considering receiver algorithms.

FIG. 3 shows a processing system 300 for generating training dataaccording to various embodiments. The processing system 300 may includethe memory device 108. The memory device 108 may store the plurality ofdata packets 104, wherein the plurality of data packets 104 may beprovided to the memory device 108 using the transmitting device 102according to the system 100A or using the simulation model 114 accordingto the processing system 100B. The processing system 300 may furtherinclude the receiving device 106. The receiving device 106 may includethe at least one processor 110. The receiving device 106 may furtherinclude a plurality of receivers 302, wherein each receiver of theplurality of receivers 302 may be configured to process data packets,such as the plurality of data packets 104. Processing a data packet by areceiver may include equalization, detection, and/or decoding (forexample joint equalization, detection and decoding). The plurality ofreceivers 302 may be configured to provide the plurality of secondclassified data packets 206, wherein the plurality of second classifieddata packets 206 may be training data for training the model 202.According to various embodiments, the plurality of receivers 302 isconfigured to process the plurality of data packets 104 and theprocessor 110 may be configured to determine the plurality of secondclassified data packets 206 based on the processing of the plurality ofdata packets 104 performed by the plurality of receivers 302.

Each receiver of the plurality of receivers 302 may include a complexitylevel of a plurality of complexity level. A complexity level maydescribe a complexity of the architecture of the respective receiver.The complexity level of a receiver may depend on the amount ofiterations needed for processing a data packet. The amount of neededoperations may be based on multiplications (MULT) and additions (ADD).The more complex a receiver (i.e. a receiver architecture) is (i.e. thehigher the complexity level of the receiver is) the higher is the powerconsumption and the delay of the receiver. Illustratively, a highercomplexity receiver is for example capable to process, such as decode,weaker signals or more complex transmission channel parameters withrespect to the data packets than a receiver having a lower complexity.In other words, illustratively, a receiver having a higher complexity iscapable of processing more complex data packets with respect to a signalstrength, a transmitted data pattern and/or channel characteristics.

According to various embodiments, each data packet of the plurality ofdata packets 104 requires a receiver of the plurality of receivers 302with a minimum complexity level (i.e. complexity level above a lowestrequired complexity level) to be processed without an error. In otherwords, each receiver of the plurality of receivers 302 having acomplexity level above the minimum complexity level is capable ofprocessing a respective data packet without an error and the processingof a respective data packet by a receiver of the plurality of receivers302 having a complexity level below the minimum complexity level leadsto an error. In other words, a receiver of the plurality of receivers302 having a complexity level below the minimum complexity level of arespective data packet may not be capable to process, for example todecode, the data packet.

FIG. 4 shows a processing system 400 for generating training dataaccording to various embodiments. The processing system 400 maycorrespond substantially to the processing system 300, wherein theplurality of receivers 302 includes a first receiver 402 and a secondreceiver 404. The complexity level of the first receiver 402 may belower than the complexity of the second receiver 404. Thus,illustratively, as described above, the second receiver 404 may becapable to process data packets having a higher complexity than the datapackets the first receiver 402 is capable to process. The at least oneprocessor 110 may be configured to generate the plurality of secondclassified data packets 206. The first receiver 402 may be configured toprocess each data packet of the plurality of data packets 104. An errorstatus 406 may be determined based on the processing of a data packet bythe first receiver 402. If the first receiver 402 processes a datapacket of the plurality of data packets 104 without an error (in otherwords, is capable to process the data packet, i.e. error status 406“No”), the second class 408 of the plurality of second classesassociated to the first receiver 402 may be assigned to the respectivedata packet. In other words, the processor 110 may be configured tolabel the respective data packet with a second class 408 associated tothe first receiver 402.

If the first receiver 402 generates an error while processing a datapacket of the plurality of data packets 104 (in other words, is notcapable to process the data packet, i.e. error status 406 “Yes”), thedata packet may be provided to the second receiver 404 and the secondreceiver 404 may be configured to process the data packet.

An error status 410 may be determined based on the processing of thedata packet by the second receiver 404. If the second receiver 404processes the data packet without an error (in other words, is capableto process the data packet, i.e. error status 410 “No”), the secondclass 412 of the plurality of second classes associated to the secondreceiver 404 is assigned to the respective data packet. In other words,the processor 110 may be configured to label the respective data packetwith a second class 412 associated to the second receiver 404.

If the second receiver 404 generates an error while processing the datapacket (in other words, is not capable to process the data packet, i.e.error status 410 “Yes”), a signal 414 may be provided, for exampleoutputted. The signal 414 may be a stop signal for stopping theprocessing of the respective data packet, or may be a warning signalthat the respective data packet could not be processed.

According to various embodiments, the plurality of receivers 302includes more the two receivers. The plurality of receivers 302 may beconfigured to process each data packet of the plurality of data packets104 and the processor 110 may be configured to assign to each datapacket a second class associated to a receiver of the plurality ofreceivers 302 including the minimum complexity level in order to processthe respective data packet without an error. The plurality of receivers302 may be configured to process each data packet of the plurality ofdata packets 104 successively starting with the receiver including thelowest complexity level. If the receiver including the lowest complexitylevel fails to process a data packet (i.e. generates an error), thereceiver of the plurality of receivers 302 including the next lowestcomplexity level processes the respective data packet. The plurality ofreceivers 302 may be configured to continue processing a data packetsuccessively using the receiver including the next lowest complexitylevel until a receiver of the plurality of receivers 302 processes therespective data packet without an error. The complexity level of thereceiver, which processes the respective data packet without an error,may be the minimum complexity level of the data packet. In others words,the minimum complexity level for processing a data packet may be definedby the receiver, which processes the respective data packet without anerror. The processor 110 may be configured to assign the second classassociated to the receiver, which processes a data packet without anerror, to the respective data packet. Thus, the plurality of receivers302 may be configured to process each data packet of the plurality ofdata packets 104 and the processor 110 may be configured to assign asecond class to each data packet of the plurality of data packets 104depending on the processing performed by the plurality of receivers 302.If none of the receivers of the plurality of receivers 302 is capable toprocess a respective data packet without an error, a signal, such as thesignal 414, may be provided, for example outputted. In other words, ifthe receiver of the plurality of receivers 302 having the highestcomplexity level generates an error while processing the data packet (inother words, is not capable to process the data packet, i.e. errorstatus “Yes”), the signal may be provided.

According to various aspects, if none of the receivers of the pluralityof receivers 302 is capable to process a respective data packet withoutan error (e.g., error status 410 “Yes”), a second class associated tothe error indicator may be assigned to the respective data packet. Inthis case, each first class of the plurality of first classes except forone first class may be associated with a respective receiver of theplurality of receivers. For example, the one first class may beassociated with an error indicator (e.g., a receiver-processing error)and the other first classes of the plurality of first classes may bebijectively assigned to a respective receiver of the plurality ofreceivers. With respect to FIG. 2, the processor 110 may be configuredto train the model 202 such that the plurality of first classified datapackets 204 generated by the model 202 corresponds to the plurality ofsecond classified data packets 206. Thus, the model 202 learns to assigna class, which is associated with a receiver of the plurality ofreceivers or with the error indicator, to a data packet considering thedata packet parameters, such as properties of a transmitted data patternand channel characteristics, and considering receiver algorithms.Illustratively, the trained model may be configured to classify areceived data packet by assigning a class to the data packet, whereinthe class indicates either, which receiver of the plurality of receiversis capable to process the data packet without an error (e.g., if theclass is associated with a receiver of the plurality of receivers), orthat none of the receivers is capable to process the data packet withoutan error (e.g., if the class is associated with the error indicator).

FIG. 5 shows a method 500 for training a model according to variousembodiments. The method 500 may include a first classifying of aplurality of data packets 104 using the model 202 (in 502). The model202 may assign a first class of a plurality of first classes to eachdata packet of the plurality of data packets 104, wherein the firstclass may be associated with a receiver of a plurality of receivers 302.In other words, a plurality of first classified data packets 204 may begenerated, wherein each data packet of the plurality of data packets 104is labeled with a first class.

The method 500 may further include a second classifying of the pluralityof data packets 104 (in 504), wherein a second class of a plurality ofsecond classes may be assigned to each data packet of the plurality ofdata packets 104. The second class may be associated with a receiver ofthe plurality of receivers 302. In other words, a plurality of secondclassified data packets 206 may be generated, wherein each data packetof the plurality of data packets 104 is labeled with a second class.

The method 500 may further include training the model 202 using theplurality of first classes and the plurality of second classes. In otherwords, the model 202 may be trained using the plurality of firstclassified data packets 204 and the plurality of second classified datapackets 206. The model 202 may be trained by comparing the plurality offirst classified data packets 204 with the plurality of secondclassified data packets 206. In other words, the model 202 may betrained by comparing the first class assigned to each data packet of theplurality of data packets 104 with the second class assigned to therespective data packet. The model 202 may be adapted such that the firstclass assigned to each data packet of the plurality of data packets 104corresponds to the second class of the respective data packet.

For example, the first class and the second class associated to areceiver may be numbers. In others words, the model 202 may beconfigured to assign a first class including a first number to a datapacket, wherein the first number may be associated to a receiver, suchas the first number “1” for a first receiver, the first number “2” for asecond receiver etc. The second classifying may include assigning asecond class including a second number to a data packet, wherein thenumber may also be associated to a receiver, such as the second number“1” for a first receiver, the second number “2” for a second receiveretc.

The second class assigned to a data packet may be training data for thedata packet. Training the model 202 may include comparing the firstclass, i.e. the first number, with the second class, i.e. the secondnumber, assigned to a data packet. Training the model 202 may furtherinclude adapting the model 202 such that the first class, i.e. the firstnumber, assigned to a data packet corresponds to the second class, i.e.the second number, of the data packet. Illustratively, the training data(plurality of second classes) may include a number associated to areceiver of a plurality of receivers and the model 202 is trained toassign the number, given by the training data, to the data packet.

FIG. 6 shows a system 600 including a trained model according to variousembodiments. The system 600 may include a transmitting device 602. Thetransmitting device 602 may correspond to the transmitting device 102.The transmitting device 602 may be configured to provide, for example totransmit, a plurality of data packets 604. The transmission propertiesof the plurality of data packets 604 may correspond substantially to thetransmission properties of the plurality of data packets 104. The system600 may further include a receiving device 606. The receiving device 606may be configured to receive the plurality of data packets 604.According to various embodiments, the transmitting device 602 isconfigured to transmit the plurality of data packets 604 wirelessly, forexample via a wireless communication channel, such as a radio frequency(RF) communication channel, and the receiving device 606 is configuredto receive the plurality of data packets 604 wirelessly from thetransmitting device 602. The receiving device 606 may include theplurality of receivers 302, wherein the plurality of receivers 302 maybe configured to process data packets. The system 600 may be a multipleinput multiple output (MIMO) system, i.e. the transmitting device 602may include a plurality of transmitters for transmitting data packetsand the receiving device 606 may include the plurality of receivers 302for receiving data packets.

The receiving device 606 may include a trained model 608. Thus, thereceiving device 606 may include at least one processor such as theprocessor 110, and the at least one processor may implement the trainedmodel 608. The trained model 608 may be trained by the method 500 fortraining a model. The trained model 608 may be configured to classifyeach data packet of the plurality of data packets 604, wherein a classis assigned to each data packet and wherein the class is associated witha receiver of the plurality of receivers 302. In other words, thetrained model 608 may be trained such that the trained model 608 selectsa receiver of the plurality of receivers 302 for a data packet, forexample for each data packet of the plurality of data packets 604,wherein the trained model 608 may select a receiver of the plurality ofreceivers 302 having the minimum complexity level required for therespective data packet. In even other words, the trained model 608 maybe configured to assign a receiver of the plurality of receivers 302 toa data packet and the assigned receiver may process the data packet.

The at least one processor of the receiving device 606 may be configuredto estimate a processing time and/or a processing cost based on theclassification of the plurality of data packets 604. The at least oneprocessor of the receiving device 606 may be configured to estimate theprocessing time and/or the processing cost based on the classificationof the plurality of data packets 604 before processing the plurality ofdata packets 604 by the plurality of receivers 302, i.e. beforeprocessing each data packet of the plurality of data packets 604 by theassigned receiver of the plurality of receivers 302. For example, the atleast one processor estimates a processing time for each receiver of theplurality of receivers and total processing time by multiplying theprocessing time for each receiver with the number of data packets of theplurality of data packets 604, which are assigned to the respectivereceiver by the trained model 608.

FIG. 7 shows a receiving device 700 including a trained model accordingto various embodiments. The receiving device 700 may correspondsubstantially to the receiving device 606, wherein the plurality ofreceivers 302 included in the receiving device 700 include a selector702 as well as at least a first receiver 704 and at least a secondreceiver 706. The selector 702 may be configured to select a receiver ofthe plurality of receivers 302, for example the first receiver 704 orthe second receiver 706, for a data packet of the plurality of datapackets 604 using the class assigned to a respective data packet by thetrained model 608. The first receiver 704 and the second receiver 706may be arranged parallel to each other. In other words, a data packetmay be provided to a receiver of the plurality of receivers 302 by theselector 702. Thus, for example, either the first receiver 704 or thesecond receiver 706 may be selected for processing the respective datapacket.

The invention claimed is:
 1. A method of computer-implemented training amodel, the method comprising: first classifying a plurality of datapackets using the model, wherein a first class is assigned to each datapacket of a plurality of data packets, wherein the first class isassociated with a receiver of a plurality of receivers, wherein eachreceiver of the plurality of receivers comprises a complexity level of aplurality of complexity levels, and wherein each data packet of theplurality of data packets requires a receiver of the plurality ofreceivers with a minimum complexity level to be processed without anerror; second classifying the plurality of data packets, wherein asecond class is assigned to each data packet of the plurality of datapackets, wherein the second class is associated with a receiver of theplurality of receivers, wherein the second classifying comprises:processing each data packet of the plurality of data packets using theplurality of receivers by successively starting with the receivercomprising the lowest complexity level and continuing with the receivercomprising the next lowest complexity level until a receiver of theplurality of receivers processes the respective data packet without anerror, wherein the complexity level of the receiver, which processes therespective data packet without an error, is the minimum complexitylevel, and assigning to each data packet of the plurality of datapackets the second class associated to a receiver of the plurality ofreceivers comprising the minimum complexity level; and training themodel using the plurality of first classes and the plurality of secondclasses assigned to the plurality of data packets.
 2. The method ofclaim 1, wherein the plurality of data packets comprises communicationdata.
 3. The method of claim 1, wherein each data packet of theplurality of data packets comprises In-phase/Quadrature data.
 4. Themethod of claim 1, wherein training the model comprises adapting themodel by comparing the first class with the second class assigned toeach data packet of the plurality of data packets.
 5. The method ofclaim 4, wherein training the model further comprises adapting the modelsuch that the first class assigned to each data packet of the pluralityof data packets corresponds to the second class.
 6. The method of claim1, wherein the model is a neural network.
 7. A receiving device,configured to perform the method of claim
 1. 8. A data reception method,comprising: classifying a plurality of data packets using a modeltrained by the method of claim 1, wherein a class is assigned to eachdata packet of the plurality of data packets and wherein the class isassociated with a receiver of a plurality of receivers; processing eachdata packet of the plurality of data packets using the receiver of theplurality of receivers associated to the respective class of theplurality of classes.
 9. The data reception method of claim 8, furthercomprising: estimating a processing time and/or processing cost based onthe classification of the plurality of data packets before processingthe plurality of data packets.
 10. A system, comprising: a receivingdevice comprising a model trained by the method of claim 1, thereceiving device configured to receive and to process data packets; atleast one transmitter device, configured to transmit data packets to thereceiving device.
 11. The system of claim 10, wherein the system is amultiple input multiple output (MIMO) system.
 12. A method ofcomputer-implemented training a model, the method comprising: firstclassifying a plurality of data packets using the model, wherein a firstclass is assigned to each data packet of a plurality of data packets,wherein the first class is associated with a receiver of a plurality ofreceivers, wherein each receiver of the plurality of receivers comprisesa complexity level of a plurality of complexity levels, and wherein eachdata packet of the plurality of data packets requires a receiver of theplurality of receivers with a minimum complexity level to be processedwithout an error; second classifying the plurality of data packets,wherein a second class is assigned to each data packet of the pluralityof data packets, wherein the second class is associated with a receiverof the plurality of receivers, wherein the second classifying comprises:processing each data packet of the plurality of data packets todetermine the respective receiver with the minimum complexity levelrequired to process the respective data packet without an error, whereinthe processing comprises processing the respective data packet by afirst receiver comprising a first complexity level and, in the case thatthe first receiver generates an error, processing the data packet by asecond receiver comprising a second complexity level higher than thefirst complexity level; and assigning to each data packet of theplurality of data packets the second class associated to therespectively determined receiver comprising the minimum complexitylevel; and training the model using the plurality of first classes andthe plurality of second classes assigned to the plurality of datapackets.
 13. The method according to claim 12, wherein the firstcomplexity level is the lowest complexity level.
 14. The methodaccording to claim 12: wherein, in the case that the second receivergenerates an error, processing the respective data packet to determinethe receiver with the minimum complexity level further comprisesprocessing the data packet by a third receiver comprising a thirdcomplexity level higher than the second complexity level.
 15. The methodaccording to claim 12: wherein, in the case that the second receiverdoes not generate an error, the second complexity level is the minimumcomplexity level.